Noise filtering inverse transformation

ABSTRACT

A method, system and apparatus for noise filtering inverse transformation (NFIT), recovering phases and amplitudes of singular cycles of data carrying tones or sub-bands from a composite signal such as OFDM, is presented herein. Such NFIT comprises adaptive inverse transformation of non-linear channel transform function and instant accommodation of time variant quickly changing characteristics of transmission channel caused by interferences including line loads, cross-talk or predictable noise.

This application claims priority benefit of U.S. Provisional ApplicationNo. 60/894,433 filed on Mar. 12, 2007.

BACKGROUND

1. Field

The present disclosure is directed to noise filters for serial data linkreceivers including all receivers for copper/optical/wireless links forlocal and remote data transmissions.

More particularly, this disclosure presents low cost high resolutionnoise filtering inverse transformation method, system and apparatus(NFIT) for precise recovery of originally transmitted signals from noisyreceived signals.

Solutions presented herein comprise systems and methods for programmablenoise filtering from over-sampled wave-forms, carrying variable lengthsdata encoding pulses, which transfer data rates ranging up to ½ oftechnology's maximum clock frequency.

The NFIT shall be particularly advantageous in system on chip (SOC)implementations of signal processing systems.

2. Background

The purpose of noise filters is to reconstruct original signal byreduction of received signal components representing noise and/or byenhancement of received signal components representing the originalsignal.

Limitations of conventional noise filtering methods and electroniccircuit technologies cause that only linear time invariant filters (LTIfilters) can be used in majority of serial communication links.

Such LTI approximations impair filtering efficiency of the majority ofthe communication links which are non-linear and time variant and havechanging in time characteristics.

Furthermore due to such limitations of conventional solutions; evenrarely used non-linear and/or adaptive filters using adaptive algorithmsto accommodate changing in time characteristics of transmissionchannels, can accommodate only limited and slowly changing portions ofsignal non-linearity and/or distortion caused by nonlinear and/orchanging in time characteristics of transmission channel.

Frequency sampling filters (FSF) capable of recovering particularsinusoidal tones/sub-bands from a composite signal such as OFDM frame,were known and described as rarely used in the book by Richard G. Lyons;“Understanding Digital Signal Processing”, Second Edition 2004 PrenticeHall.

However such frequency sampling filters and other conventional frequencydomain methods do not have time domain solutions needed to preserve andrecover phase alignments of singular cycles of tones/sub-bands to thecomposite signal frame, wherein such phase alignments carry phases oftones/sub-bands transmitting data encoded originally.

It is the objective of solutions presented herein to alleviate suchlimitations by contributing;

accommodation of unlimited non-linearity and time variant quick changesof transmission channel such as those caused by line load, cross-talkand inter-band interference from adjacent transmission channels,

and said time domain solutions combining signal processing in frequencydomain and time domain, in order to enable recovery of phases andamplitudes of singular cycles or half-cycles of data carrying tones orsub-bands comprised in the composite signal.

SUMMARY

The present disclosure eliminates said fundamental deficiencies ofconventional noise filters as it is explained below.

This disclosure comprises a recovery of the originally transmittedsignal by reversing functioning of the transmission channel whichdistorts and introduces interferences to the transmitted signal, whereinsuch reversal is accomplished by applying an inverse transformation to areceived signal distorted by the transmission channel additionallyintroducing transmission link noise and adjacent channels/bandsinterferences.

Since every transmission channel performs some kind of transformation ofa transmitted signals space consisting of originally transmitted shapesinto a received signal space substantially different due to transmissionchannel distortion and interferences, such inverse transformation canprovide effective universal means for noise filtering recovery of theoriginal signal.

Consequently this disclosure includes a method, a system and anapparatus for noise filtering inverse transformation of a receivedsignal (NFIT) which eliminates non-linear and other distortions andinterferences of the transmission channel from the received signal, bycomprising:

capturing an over-sampled received signal waveform;

pre-filtering and/or analyzing such captured waveform in order to detectwhich predefined set of waveform shapes includes the analyzed waveform,wherein such predefined set of shapes is designed as implementing aknown transformation of an originally transmitted signal;

a recovery of said originally transmitted signal (freed of transmittingchannel distortions and interferences) by applying the inversetransformation of said known transformation of the original signal intothe predefined sets of shapes;

wherein such original signal transformation and its inversetransformation can be derived as theoretical models and/or results oftraining session and/or results of adaptive optimization process.

The NFIT further comprises comparing such captured waveform shape with aframe of reference (named also as mask) characterizing such set ofpredefined shapes in order to verify which set of predefined shapes thecaptured shape shall belong to, wherein:

an captured waveform interval is compared with such mask by producing anestimate of their shapes similarity (named also as proximity estimate),such as correlation integral or deviation integral between samplesbelonging to the waveform and their counterparts belonging to the mask;

such estimate of shapes similarity (proximity estimate) is used todetect, if the set of shapes characterized by the mask used correspondsto the captured waveform;

the inverse transformation of the known transmission channeltransformation is applied to the mask characterizing such correspondingset of shapes, in order to recover the original signal free oftransmission channel distortions and interferences.

Such NFIT further includes an instant accommodation of time variantquickly changing characteristics of transmission channel, caused byinterferences such as line loads or cross-talk or inter-bandinterference; by comprising:

producing real time evaluations of such instantly changinginterferences;

using such real time evaluations for selection of a different frame ofreference accommodating such instant interferences;

using such selected different frame for said detection of set of shapescorresponding to the captured waveform affected by the instantinterferences;

said recovery of the original signal by performing the inversetransformation of the frame of reference characterizing such detectedset of shapes.

The NFIT includes a method for data recovery from received DMT orMulti-band frames wherein such data recovery method comprises anintegration of frequency domain and time domain signal processingmethods, wherein:

a DMT or Multi-band signal is filtered in frequency domain whereinfrequency filters are used to produce filtered signals having knownphase relation to said DMT or Multi-band frame (signals are filtered inphase with the frame);

said time domain time signal processing of such frequency filteredsignals performed in phase with the frame measures amplitudes and/orphases of singular half-cycles or cycles of DMT or Multi-band tones;

such measured amplitudes and/or phases are inversely transformed byreversing their distortions introduced by a transmission channel, ofsaid DMT or Multi-band composite signal, including distortions ofprevious processing stages;

such inversely transformed amplitudes and phases are used to recoverdata symbols originally encoded in the transmitted signal;

every set of data symbols recovered from amplitudes and/or phases of aparticular tone or band-frequency, is processed using statisticalmethods in order to select the most probable symbol.

Such data recovery method includes sensing amplitudes and/or phases ofsignals surrounding said data carrying tone or band signal and reversingdistortions of such data signal caused by said surrounding signals,wherein such data recovery method comprises:

detection of amplitudes and/or phases of noise and/or other signalssurrounding such particular data carrying signal in frequency domainand/or in time domain;

using such detected amplitudes and/or phases of noise and/or othersurrounding signals for deriving estimates of data signal distortionsintroduced by the surrounding noise and/or other signals;

using such distortions estimates for performing reverse transformationof said detected amplitudes and/or phases of the data carrying signalinto the amplitudes and/or phases corresponding to the data signaltransmitted originally.

This disclosure further includes using a synchronous circular processing(SCP) method and apparatus for very fast front-end signal processingperforming real time functions, such as:

continuous waveform over-sampling and capturing, said instantinterferences evaluations, waveform interval analysis and comparisonwith pre-selected masks and other signal processing in time domainand/or frequency domain.

The SCP comprises sequentially connected stages fed with digital samplesproduced by A/D converter of incoming wave-form; wherein:

such sequential stage comprises a register for storing and/or processingsequentially multiple wave-form samples;

such sequential stage register comprises circularly used segmentsdesignated for storing and/or processing of consecutive samples assignedto consecutive segments of the stage by an index circulating within astage segments number;

such storing and/or processing of consecutive samples in the segments ofthe sequential stage is driven by consecutive circular clocks designatedfor a particular stage wherein every such consecutive circular clock isapplied to its designated segment at a time instant occurringperiodically within a circulation cycle.

outputs of a segment or segments of this sequential stage register canbe used by a next sequential stage while other segment or segments ofthe first stage are still accepting and/or processing other samplesduring the first stage's circular cycle.

Such SCP enables configuration comprising variety of said consecutivestages having widely varying sizes of registers defined by the stagessegments numbers adjusted to accommodate data processing requirementsbetween the consecutive stages.

Consequently; a number of said stage segments can be adjusted freely toaccommodate FIR and/or IIR filters having different orders, and anyrequired extension of processing time can be achieved by adding acorresponding sampling intervals' number by increasing number ofsegments in the stage register.

The SCP further comprises;

using such SCP stage for implementing a digital FIR or IIR filterproducing output which maintains a known phase displacement towards (isin phase with) a composite input signal;

using such SCP stages for implementing time domain filters producingoutputs which are in phase with the composite input signal;

and combining such stages implementing such FIR and/or IIR filtersand/or such time domain filters, into a signal processing systemproducing multiple outputs which are in phase with the composite inputsignal.

This disclosure still further includes using a programmable control unit(PCU) for on-line back-up processing providing very comprehensive andversatile programmable functions such as:

controlling operations of received waveform screening and capturingcircuit (WFSC) providing received waveform samples needed for saidreceived waveform analysis which uses a training session and/orimplements an adaptive noise filtering procedure updating inversetransfer function while usual received signal filtering is still takingplace uninterrupted;

calculating and implementing the inverse transformation function basedon received waveform analysis;

pre-loading said masks used by the SCP;

controlling all said SCP operations by pre-loading SCP control registerswhich define functions performed by SCP.

The NFIT further includes an inverse transformation of band filteredsignal (ITBFS) method, system and apparatus for recovering the originalsignal from a received signal pre-filtered by a band-pass filter whereinthe inverse transformation is applied to such pre-filtered signal inorder to compensate transmission channel changes caused by instantinterferences such as inter-band interferences and/or line loads and/orcross-talk; wherein the ITBFS comprises:

using a band-pass filter for producing a pre-filtered received signalfrom the captured waveform;

producing real time evaluations of such instant interferences which arein-phase with the pre-filtered signal;

using such real time evaluations for selection of a different frame ofreference accommodating such instant interferences;

using such selected different frame for said detection of set of shapescorresponding to the pre-filtered signal affected by the instantinterferences;

said recovery of the original signal by performing the inversetransformation of the frame of reference characterizing such detectedset of shapes.

The ITBFS includes said producing of real time evaluations furthercomprising:

a time domain processing of the captured waveform and/or otherpre-filtered frequency bands adjacent to the pre-filtered signal band,wherein such time domain processing of the waveform and/or the adjacentfrequency bands produce results which are in phase with the pre-filteredsignal in order to accomplish in-phase application of said selectedframe of reference to the pre-filtered signal.

The ITBFS further includes:

using said SCP for implementing a band-pass filter producing apre-filtered received signal from the captured waveform;

using said SCP for such time domain processing of the captured waveformand/or other pre-filtered frequency bands adjacent to the pre-filteredsignal band, in order to produce said results being in phase with thepre-filtered signal.

This disclosure comprises using NFIT and ITBFS for recovering theoriginally transmitted signal in ADSL/VDSL wireline systems; bycomprising the steps of:

using said SCP for implementing band-pass filters for pre-filtering ofdiscrete tone signals from the captured waveform;

using said SCP for said time domain processing of the captured waveformand/or such pre-filtered tone signals adjacent to a specificpre-filtered tone signal, wherein such time domain processing of thewaveform and/or the adjacent pre-filtered tone signals produce resultswhich are in phase with the specific pre-filtered tone signal in orderto accomplish in-phase application of said selected frame of referenceto the specific pre-filtered tone signal.

wherein by applying such steps to every specific discrete tone signal,all originally transmitted discrete tone signals are recovered.

Furthermore this disclosure comprises using NFIT and ITBFS forrecovering originally transmitted signal in a wide variety of othercommunication systems as well; including wireless communication systemssuch as Multi-Band systems and WiLAN.

The NFIT further includes inverse transformation of frequency domainrepresentation of received signal (ITFDR) method and system forfiltering out noise from a received signal frequency spectrum and forcorrecting transmission channel transformation by an inversetransformation of the received signal spectrum into an originallytransmitted signal; wherein:

such received frequency spectrum is compared with a spectrum mask, byproducing a spectrum proximity estimate such as a correlation integralor deviation integral between components of the received frequencyspectrum and their counterparts from the spectrum mask;

such proximity estimate is used to detect, if the spectrum mask appliedrepresents a noise filtered version of the received frequency spectrum;

said inverse transformation of said transmission channel transformationis applied to the spectrum mask representing such noise filteredreceived spectrum, in order to recover the originally transmittedsignal.

Such ITFDR comprises:

using a variety of spectrum masks for approximating different saidreceived spectrums and/or for filtering out different predictable and/orrandom noise components;

using multiple consecutive proximity estimates for identifying suchspectrum mask which is most effective in said approximating of thereceived spectrum and in said noise filtering;

or using proximity estimates providing indication which spectrum maskshall be applied as the next in order to detect such most effectivespectrum mask;

wherein the selection of the next applied spectrum mask is determined byresults of real-time in-phase processing of the received signal and/orreceived spectrum and/or by previous proximity estimates.

The ITFDR method and system includes recovering the original signal froma received signal wherein Fast or Discrete Fourier Transform of receivedsignal, in such communication systems as wireless multi-band OFDM(Orthogonal Frequency Division Multiplexing) or wireline ADSL/VDSL DMT,is inversely transformed in order to recover the content of originallytransmitted data carrying signal; wherein the ITFDR comprises:

producing real time evaluations of frequency spectrums of majorinterferences affecting frequency bands or discrete tones reproduced byFFT/DFT, wherein such major interferences include said re-produced byFFT/DFT frequency spectrums influencing themselves and adjacentfrequency bands/tones or other significant interferences; using suchreal time evaluations for a selection of a spectrum mask most suitablefor accommodating such major interference sources when it is applied toa specific band/tone reproduced by FFT/DFT;

wherein a specific band/tone spectrum reproduced by FFT/DFT is comparedwith such selected spectrum mask, by producing a spectrum proximityestimate such as correlation integral or deviation integral betweencomponents of the band/tone spectrum and their counterparts from thespectrum mask;

wherein such proximity estimate is used to detect, if the set ofspectrums characterized by the spectrum mask corresponds to the specificband/tone spectrum;

the inverse transformation of the known transmission channeltransformation is applied to the spectrum mask characterizing suchcorresponding set of spectrums, in order to recover the originalfrequency domain signal free of transmission channel interferences;

wherein by applying such steps to every specific band/tone spectrum, theoriginal frequency domain signals transmitted over all bands/tones arerecovered.

The ITFDR further comprises:

analyzing frequency spectrums produced by the FFT or DFT during atraining session or adaptive control procedure in order to derive saidspectrum masks corresponding to specific data symbols transmitted overspecific frequency bands or discrete tones, wherein for every specificdata symbol variety of different spectrum masks can be derived whereinsuch different spectrum masks correspond to different content of majorsources of interference such as adjacent frequency bands or discretetones;

wherein such derivation of the spectrum masks includes derivation of aninverse transform of transmission channel transformation which can beused for said recovery of original frequency domain signals based on ananalysis of said corresponding to them frequency spectrums reproduced byFFT/DFT on the receiver side.

The NFIT introduced with above examples, further includes methods andsystems characterized below.

The NFIT includes inverse transformation of received signal (ITRS)method and system and apparatus, for recovering originally transmittedsignal and for filtering out a transmission channel transformation andnoise by applying an inverse transformation of said received signal intothe original signal; wherein:

a representation of said received signal is compared with a mask, byproducing an proximity estimate such as correlation integral ordeviation integral between components of the received signalrepresentation and their counterparts from the mask;

such proximity estimate is used to detect, if the mask appliedrepresents a noise filtered version of the received signalrepresentation;

said inverse transformation of said transmission channel transformationis applied to the mask representing such noise filtered received signal,in order to recover the originally transmitted signal.

Said ITRS comprises:

using variety of such masks for approximating different representationsof said received signal and/or for filtering out different predictableand/or random noise components;

and/or using results of real-time in-phase processing of suchrepresentation of received signal or representations of predictableinterferences, for such selections of next masks applied;

using multiple consecutive proximity estimates for selecting said maskwhich is most effective in said approximating of the received signalrepresentation and in said noise filtering;

and/or using a numerical result of a previous proximity estimate forselecting such most effective mask.

Said ITRS further comprises:

using an over-sampled waveform of a time interval of received signal assaid received signal representation;

or using a frequency spectrum of said time interval of received signalas said received signal representation.

Said ITRS using the over-sampled interval waveform as signalrepresentation, comprises a preliminary characterization of a shape ofwaveform interval wherein a result of such interval shapecharacterization facilitates selection of an interval mask applied tothe interval waveform in order to find a correct approximation of thereceived signal interval freed of unpredictable noise; wherein such ITRScomprises:

evaluation of an averaged peak amplitude of internal pulses occurringwithin the interval waveform;

evaluation of an averaged phase, of periodical edges of such internalinterval pulses, versus a known phase reference;

using such averaged amplitude and averaged phase for selecting saidinterval mask.

The ITRS comprises applying plurality of such masks and analyzing theirproximity estimates in order to optimize selection of a final intervalmask transformed inversely into the original signal; wherein such ITRScomprises:

applying said interval masks having different amplitudes and analyzingtheir proximity estimates in order to find an interval mask providingthe closest amplitude wise approximation of the interval waveform;

and/or applying said interval masks having different phases andanalyzing their proximity estimates in order to find an interval maskproviding the closest phase wise approximation of the interval waveform;

inverse transformation of the interval mask, providing such closestapproximation, into the originally transmitted signal.

The ITRS further comprises different application methods of saidinterval masks having different phases versus said received signalwaveform; wherein:

plurality of said interval masks, having different phases versus thesame interval waveform, is applied and their proximity estimates areanalyzed in order to find the closest phase wise approximation of theinterval waveform;

or the same interval mask is applied to plurality of phase shiftedinterval waveforms and resulting proximity estimates are analyzed, inorder to find the optimum phase of the interval mask versus the receivedsignal waveform approximated by that mask.

This disclosure comprises methods, systems and solutions describedbelow.

1. A method for noise filtering inverse transformation (NFIT),comprising a recovery of an original signal by reversing functioning ofthe transmission channel distorting the original signal, wherein suchreversal is accomplished by applying an inverse transformation to areceived signal wherein such distortion includes transmission link noiseand adjacent channel or band interference; the NFIT method comprisingthe steps of:

capturing an over-sampled waveform of said received signal;

pre-filtering or analyzing such captured waveform in order to detectwhich predefined set of waveform shapes includes pre-filtered oranalyzed waveform, wherein such predefined set of shapes is designed asimplementing a known transformation of the original signal;

the recovery of said original signal by applying the inversetransformation of said known transformation of the original signal intothe predefined sets of shapes;

wherein such original signal transformation and its inversetransformation can be derived using theoretical models or results oftraining session or results of adaptive optimization process.

2. A method for noise filtering inverse transformation (NFIT),comprising a recovery of an original signal by reversing functioning ofthe transmission channel distorting the original signal, wherein suchreversal is accomplished by applying an inverse transformation to areceived signal wherein such distortion includes transmission link noiseand adjacent channel or band interference; the NFIT method comprisingthe steps of:

capturing an over-sampled waveform of said received signal; comparing aninterval of the captured waveform with a mask used as frame of referencecharacterizing a set of predefined shapes, in order to verify which setof predefined shapes such waveform interval corresponds to,

wherein such comparison includes

producing a proximity estimate, estimating similarity of their shapes,such as correlation integral or deviation integral between samplesbelonging to the waveform interval and their counterparts belonging tothe mask,

and using such proximity estimate for verifying if the set of shapescharacterized by the mask used corresponds to the captured waveformshape;

applying the inverse transformation of the known transmission channeltransformation to the mask characterizing such corresponding set ofshapes, in order to recover the original signal.

3. A method for noise filtering inverse transformation (NFIT),comprising a recovery of an original signal by reversing functioning ofthe transmission channel distorting the original signal, wherein suchreversal, accomplished by applying an inverse transformation to areceived signal, includes accommodation of time variant quickly changingcharacteristics of transmission channel caused by interference includingline load or cross-talk or inter-band interference; the NFIT methodcomprising the steps of:

capturing an over-sampled waveform of a received signal;

producing a real time evaluation of such instantly changinginterference;

using such evaluation for pre-selection of a mask used as frame ofreference characterizing a set of predefined shapes of said receivedsignal, in order to accommodate such interference;

comparing an interval of the captured waveform with such pre-selectedmask, in order to verify which set of predefined shapes such waveforminterval corresponds to,

wherein such comparison includes producing a proximity estimate,estimating similarity of their shapes, such as correlation integral ordeviation integral between samples belonging to the waveform intervaland their counterparts belonging to the mask,

said recovery of the original signal by performing the inversetransformation of the frame of reference characterizing such detectedset of shapes.

4. A method for data recovery from a composite frame (DRCR) comprisingdiscrete multiple tones (DMT) or multiple sub-bands (MSB) wherein suchdata recovery method comprises combination of frequency domain and timedomain signal processing methods, the DRCR method comprising the stepsof:

frequency domain filtering of a composite signal carrying such compositeframe wherein frequency filters produce discrete tones or sub-bandshaving known phase relation to said DMT or MSB frame i.e. said frequencyfilters keep their outputs in phase with the composite frame;

said time domain time signal processing of such discrete tones orsub-bands performed in phase with the composite frame, measuresamplitudes and/or phases of singular half-cycles or cycles of discretetones or sub-bands;

such measured amplitudes and/or phases are inversely transformed byreversing their distortions introduced by a transmission channel of saidcomposite signal, including distortions caused by previous processingstages;

such inversely transformed amplitudes and phases are used to recoverdata symbols originally encoded in the transmitted signal.

5. A DRCR as described in clause 4, wherein the DRCR method comprisesthe step of: selecting the most probable symbol by using statisticalmethods for processing a set of data symbols recovered from amplitudesand/or phases of a particular tone or sub-band.

6. A method for data recovery from a composite signal (DRCS) comprisingdiscrete multiple tones (DMT) or multiple sub-bands (MSB) wherein acombination of frequency domain and time domain signal processingmethods is utilized, wherein amplitudes and/or phases of signalssurrounding a particular data carrying tone or sub-band are sensed anddistortions of said particular tone or sub-band are reversed; the DRCSmethod comprising the steps of:

frequency domain filtering of said composite signal producing discretetones or sub-bands having known phase relation to said DMT or MSB frame;

said time domain signal processing of such discrete tones or sub-bandsmeasures amplitudes and/or phases of singular half-cycles or cycles ofdiscrete tones or sub-bands;

detection of amplitudes and/or phases of noise and/or other signalssurrounding such particular tone or sub-band, performed in frequencydomain and/or in time domain;

using such detected amplitudes and/or phases for deriving estimates ofdistortions introduced by the surrounding noise and/or other signals tosuch particular tone or sub-band;

using such distortions estimates for performing reverse transformationof said detected amplitudes and/or phases of such singular half-cyclesor cycles of the particular tone or sub-band into the amplitudes and/orphases corresponding to data transmitted originally.

such reversely transformed amplitudes and phases are used to recoverdata symbols originally encoded in the transmitted signal.

7. A synchronous circular processing (SCP) system for a front-end signalprocessing performing real time functions including continuousover-sampling and capturing of a received signal waveform, and timedomain processing of the captured waveform producing outputs having aknown phase displacement towards the received signal i.e. being in phasewith it; the SCP system comprising:

sequentially connected stages fed with digital samples produced by anA/D converter of received wave-form;

such sequential stage comprises a register for storing and/or processingsequentially multiple wave-form samples;

such sequential stage register comprises circularly used segmentsdesignated for storing and/or processing of consecutive samples assignedto consecutive segments of the stage by an index circulating within astage segments number;

such storing and/or processing of consecutive samples in the segments ofthe sequential stage is driven by consecutive circular clocks designatedfor the particular stage wherein every such consecutive circular clockis applied to its designated segment at a time instant occurringperiodically within a circulation cycle;

outputs of a segment or segments of this sequential stage register canbe used by a next sequential stage while other segment or segments ofthe first stage are still accepting and/or processing other samplesduring the first stage's circular cycle.

8. A synchronous circular processing (SCP) system for a front-end signalprocessing performing real time functions including continuousover-sampling and capturing of a received signal waveform, and timedomain processing of the captured waveform producing outputs having aknown phase displacement towards the received signal i.e. being in phasewith it; the SCP system comprising:

sequentially connected stages fed with digital samples produced by anA/D converter of received wave-form;

such sequential stage comprises a register for storing and/or processingsequentially multiple wave-form samples;

such sequential stage register comprises circularly used segmentsdesignated for storing and/or processing of consecutive samples assignedto consecutive segments of the stage by an index circulating within astage segments number;

such storing and/or processing of consecutive samples in the segments ofthe sequential stage is driven by consecutive circular clocks designatedfor the particular stage wherein every such consecutive circular clockis applied to its designated segment at a time instant occurringperiodically within a circulation cycle;

outputs of a segment or segments of this sequential stage register canbe used by a next sequential stage while other segment or segments ofthe first stage are still accepting and/or processing other samplesduring the first stage's circular cycle;

wherein said consecutive stages have varying sizes of registers definedby the stages segments numbers adjusted to accommodate data processingrequirements between consecutive stages;

wherein plurality of said stage segments accommodates FIR and/or IIRfilters which may be of different orders, and any required extension ofprocessing time can be achieved by adding a corresponding samplingintervals number by increasing number of segments in the stage register.

9. A synchronous circular processing (SCP) method for a front-end signalprocessing performing real time functions including continuousover-sampling and capturing of a received signal waveform and timedomain processing of the captured waveform producing outputs having aknown phase displacement towards the received signal; the SCP methodcomprising the steps of:

using sequentially connected stages for storing or processing of samplesproduced by A/D converter of received wave-form;

wherein such sequential stage comprises a register for storing and/orprocessing multiple sequential waveform samples;

wherein such sequential stage register comprises circularly usedsegments designated for storing and/or processing of consecutive samplesassigned to consecutive segments of the stage by an index circulatingwithin a stage segments number;

driving such storing and/or processing of consecutive samples in thesegments of the sequential stage, by applying consecutive circularclocks designated for the particular stage wherein every suchconsecutive circular clock is applied to its designated segment at atime instant occurring periodically within a circulation cycle;

using outputs of a segment or segments of a particular sequential stageregister by a next sequential stage when other segment or segments ofsaid particular stage are still accepting and/or processing othersamples during said circular cycle of the particular stage.

10. A synchronous circular processing (SCP) method for signal processingimplementing digital FIR and/or IIR filter producing outputs whichmaintain known phase displacements towards a composite input signal suchas discrete multi-tone (DMT) or multi-sub-band (MSB) i.e. said outputsare in phase with the composite signal; the SCP method comprising thesteps of:

using sequentially connected stages for storing or processing of samplesproduced by an A/D converter of received wave-form;

wherein such sequential stage comprises a register for storing and/orprocessing multiple sequential samples fed circularly into consecutivesegments of such register by consecutive circular clocks;

using outputs of a segment or segments of a particular sequential stageregister by a next sequential stage when other segment or segments ofthe particular stage are still accepting and/or processing other samplesduring a circular cycle of the particular stage;

using such sequential stage for implementing digital FIR or IIR filterproducing output which maintains a known phase displacement towards saidcomposite signal;

or using such sequential stage for implementing time domain filtersproducing outputs which are in phase with the composite signal.

11. A method of adaptive synchronous circular processing (ASCP)combining use of a front end synchronous circular processor (SCP)performing real time processing of a received signal, with aprogrammable control unit (PCU) providing back-up processing enablingmore comprehensive programmable functions; the ASCP method comprisingthe steps of:

using sequentially connected stages for storing or processing of samplesproduced by an A/D converter of a received signal wave-form;

wherein such sequential stage comprises a register for storing and/orprocessing multiple sequential samples fed circularly into consecutivesegments of such register by consecutive circular clocks;

using outputs of a segment or segments of a particular sequential stageregister by a next sequential stage when other segment or segments ofthe particular stage are still accepting and/or processing other samplesduring a circular cycle of said particular stage;

using said PCU for controlling operations of a circuit for receivedwaveform screening and capturing (WFSC),

wherein such WFSC supplies said PCU with received waveform samplesneeded for a received waveform analysis;

using said PCU for updating inverse transformation function by utilizinga training session and/or implementing an adaptive noise filteringprocedure while received signal processing is taking placeuninterrupted;

using said PCU for calculating and implementing an inversetransformation function based on said received waveform analysis,wherein such inverse transformation reverses received signal distortionsintroduced by a transmission channel of the received signal;

wherein said PCU implements such inverse transformation by controllingSCP operations by pre-loading SCP control registers which definefunctions performed by SCP.

12. A method of inverse transformation of a band filtered signal (ITBFS)for a recovery of an original signal by applying an inversetransformation to a pre-filtered signal, wherein such inversetransformation includes compensation of transmission channel changecaused by an instant interference; wherein the ITBFS method comprisesthe steps of:

using a band-pass filter for producing a pre-filtered signal from acaptured waveform of a received signal;

producing real time evaluation of such instant interference whichremains in phase with the pre-filtered signal by having a known phasealignment to such signal;

using such real time evaluation for selection of frames of referenceaccommodating such instant interference,

using such selected frames of reference for detecting a set of shapescorresponding to the pre-filtered signal affected by the instantinterference,

wherein such detection includes comparison of such frames of reference,characterizing sets of predefined signal shapes, with a shape of saidaffected signal;

said recovery of the original signal by performing the inversetransformation of the frame of reference which detected such set ofshapes corresponding to the signal affected by interference.

13. An ITBFS method as described in clause 12, wherein said bandfiltered signal is a tone or sub-band and said received signal is acomposite OFDM signal transmitted over a wireline or wireless link andsaid original signal is the original tone or sub-band incorporated intothe composite OFDM signal.

14. A method for reversal of non-linearity (RNL) of amplifier gain orsignal attenuation by applying a polynomial transformation to anon-linear signal, wherein the RNL method comprises the steps of:

identification of dependency between amplitude of an original signal andsaid non-linear signal;

using such dependency for defining polynomial approximation thresholdsand their slope coefficients and their exponents;

calculating an exponential component for every said threshold exceededby a sample of said non-linear signal,

wherein such exponential component for the maximum exceeded threshold,is calculated by rising a difference, between the non-linear signalsample and the maximum threshold, to a power defined by said exponentfor the maximum threshold;

wherein such exponential component for every other exceeded threshold,is calculated by rising a difference, between the next exceededthreshold and such other exceeded threshold, to a power defined by saidexponent for the other exceeded threshold;

calculating an approximation component for every such approximationthreshold exceeded by said non-linear signal sample, by multiplying suchexponential component by its slope coefficient;

addition of such approximation components, calculated for approximationthresholds exceeded by or equal to the non-linear signal sample;

wherein by such addition of the approximation components, calculated forthe approximation thresholds exceeded by or equal to the non-linearsignal sample, said non-linearity is reversed.

15. A method for inverse normalization (IN) of OFDM tone or sub-band,comprising reversal of frequency dependent distortion of said tone orsub-band, by applying normalizing coefficients adjusted to a frequencyof this tone or sub-band in order to equalize amplitude and phasedistortions introduced to the tone or sub-band by an OFDM transmissionchannel and a signal processing applied; such IN method comprises thesteps of:

identification of the frequency related amplitude or phase distortion ofsuch tone or sub-band by sampling and analyzing of a waveform performedby a programmable control unit (PCU);

calculating such normalizing coefficients for the tone or sub-band,performed by said PCU;

using such normalizing coefficients for equalizing such frequencyrelated distortions, performed by a real-time processing unit (RTP).

16. A method for noise compensation (NC) for a data carrying tone orsub-band of OFDM signal, utilizing evaluation of a noise patternoccurring around this tone or sub-band; wherein such NC method comprisesthe steps of:

detecting a noise pattern occurring in frequency domain by usingfrequency domain processing such as Frequency Sampling Filter (FSF) fornoise sensing in a frequency spectrum incorporating this tone orsub-band;

detecting a noise pattern occurring in time domain by using time domainprocessing for noise sensing over time intervals including this tone orsub-band;

using a programmable control unit (PCU)

for analyzing such noise pattern in frequency domain or in time domain,in order to create a deterministic or random noise model for suchfrequency domain or time domain noise pattern,

and for deriving a noise compensation coefficient by utilizing suchdeterministic or random model;

using such noise compensation coefficient by a Real Time Processor (RTP)for improving signal to noise ratio in the data carrying tone orsub-band;

using the RTP for time domain recovery of data symbols from singularsinusoidal cycles of the tone or sub-band,

applying such noise model for estimating probability of symbolsrecovered or for dismissing symbols accompanied by high noise levelsclose in time;

using such probability estimates or dismissals of unreliable symbols forapplying statistical methods for a final recovery of an original datasymbol, transmitted by the tone or sub-band, from such plurality of datasymbols recovered from the singular sinusoidal cycles.

17. A method for time domain data recovery (TDDR) from a tone orsub-band of OFDM composite frame comprising recovery of data symbolsfrom singular sinusoidal cycles or half-cycles of the tone or sub-band;the TDDR method comprising the steps of:

measuring an amplitude of a singular half-cycle or cycle by calculatingan integral of amplitude over a half-cycle or cycle time period;

measuring a phase of the half-cycle or cycle;

comparing said measured amplitude to predefined amplitude thresholds, inorder to decode an amplitude related factor;

comparing said measured phase to predefined phase thresholds, in orderto decode a phase related factor;

using such amplitude and/or phase related factor for producing anaddress to a content addressed memory (CAM) storing a counter ofhalf-cycles or cycles detecting occurrences of a particular data symbolduring said OFDM composite frame;

wherein such CAM can accommodate plurality of such counters ofoccurrences of data symbols detected within this tone or sub-band duringthe OFDM frame;

application of statistical methods for selecting data symbol recoveredfrom this tone or sub-band, by utilizing content of such counters ofdata symbols occurrences within this tone or sub-band.

It is understood that other embodiments of the present invention willbecome readily apparent to those skilled in the art from the followingDetailed Description, wherein various embodiments of the invention areshown and described by way of illustration. As will be realized, theinvention is capable of other and different embodiments and its severaldetails are capable of modification in various other aspects, allwithout departing from the spirit and scope of the present invention.Accordingly, the drawings and Detailed Description are to be regarded asillustrative in nature and not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows Block Diagram of Inverse Transformation Method in order tointroduce major sub-systems defined in FIG. 2-FIG. 11.

FIGS. 12A & FIG. 12B define Timing Clocks driving the sub-systems shownin FIG. 2-FIG. 11, wherein:

FIG. 12A shows time slots assigned for sub-clocks driving consecutiveprocessing stages,

FIG. 12B shows sub-clocks driving consecutive bits of circularprocessing stages.

The FIG. 2-FIG. 11 are numbered correspondingly to the flow of processeddata.

All interconnect signals between these figures have unique namesidentifying their sources and destinations explained in the DetailedDescription utilizing the same names.

Inputs supplied from different drawings are connected at the top or leftside and outputs are generated on the bottom due to the top-down orleft-right data flow observed generally.

Clocked circuits like registers or flip-flops are drawn with two timesthicker lines than combinatorial circuits like arithmometers orselectors.

FIG. 2 shows sampling of DMT signal and correction of it'snon-linearity.

FIG. 3 shows comb filtering of DMT signal.

FIG. 4 shows Resonating IIR Filter for 129 Tone (129T/RIF).

FIG. 4A shows Resonating IIR Filter 129.5 SubTone (129.5ST/RIF).

FIG. 5 shows integration & amplitudes registration for half-cycles of129 Tone.

FIG. 5A shows integration & amplitudes registration for half-cycles of129.5 Sub-Tone.

FIG. 6 shows phase capturing and initialization of tone processing for129 Tone/128.5 Sub-Tone/129.5 Sub-Tone, wherein block 8 shows the FrameSamples Counter and MTP_Start generator common for all Tones/Sub-TonesReal Time Processors.

FIG. 7 shows retiming & averaging of positive and negative half-cyclesfor 129 Tone/128.5 Sub-Tone/129.5 Sub-Tone.

FIG. 8 shows amplitude & phase normalization for 129 Tone/128.5Sub-Tone/129.5 Sub-Tone.

FIGS. 9A-FIG. 9B show accessing noise compensation coefficients for 129Tone.

FIG. 10 shows using these coefficients for compensating expected noisecontributions from the 128.5 Sub-Tone & 129.5 Sub-Tone.

FIG. 11 shows Recovery and Registration of 129T Frame Symbols.

DETAILED DESCRIPTION

The Inverse Transformation Method (ITM) is introduced in FIG. 1 asincluding subsystems shown in blocks 1-7.

These subsystems enable an efficient low-power processing of high-speedoversampled data, by implementing real-time processing units (RTPs)which use simplified algorithms based on variable coefficients.

These RTPs are controlled by a Programmable Control Unit (PCU) whichperforms a background processing. This background processing includesimplementing adaptive non-linear algorithms which analyze received linesignal and intermediate processing results, in order to define suchcoefficients and to download them to content addressed memories such asthe Control Register Set for 129 Tone (mentioned further below as129T_CRS occurring in FIG. 4, FIG. 6 and FIG. 7).

These memories are accessed by the RTPs implementing said ITM outlinedin FIG. 1. These RTPs can be implemented as it is detailed below for 129Tone of DMT Frame.

The RTPs include doing basic sorting of recovered symbols (introduced inthe block 7 of FIG. 1 and detailed in FIG. 11) based on symbolsoccurrence frequencies and noise levels in surrounding sub-tones ortones, while the PCU comprises doing further analysis of such sortedsymbols including use of adaptive statistical methods for finalizingselection of most credible symbols.

Said blocks 1-7 are defined in greater detail in FIG. 2-FIG. 11 andcorresponding descriptions, as it is referenced below:

block 1 comprising the PAAR Correction, is detailed in FIG. 2 anddescribed further below;

block 2 with the diagram of frequency magnitude response of itsfrequency sampling filters (shown on the right side) is detailed in FIG.3, FIG. 4, FIG. 4A and described further below;

block 3 with the diagram illustrating detection of amplitudes and phasesof tones & sub-tones (shown on the right side), is detailed in FIG. 5,FIG. 5A, FIG. 6, FIG. 7 and described further below;

block 4 is detailed in FIG. 8;

block 5 is detailed in FIG. 9A-FIG. 9B and FIG. 10;

blocks 6 and 7 are detailed in FIG. 11.

The embodiments presented herein are based on the assumption listedbelow:

-   -   DMT OFDM Frame has frequency 4 kHz.    -   DMT Frame comprises OFDM Tones numbered from 32 to 255 (such        OFDM Tones have frequencies equal to Tone_NR×4 kHz)    -   The sampling clock 0/Clk (see FIG. 2) is kept in phase with the        DMT Frame, and has sampling frequency 4 kHz×255×16=16.32 Mhz.

The NFIT (see FIG. 1) comprises a correction of Peak to AverageAmplitude Ratio (PAAR), which reverses a non-linear line signaldistortion caused by a gain limitation of line amplification path when acomposition of tones having different frequencies & phases ascends intoextreme amplitude levels.

The PAAR correction is explained below.

For Ys=Modulus(A/D sample), Ylt=Linearity Threshold, Cs=CompensationSlope;

Yc=Corrected Sample Modulus is calculated as Yc=f(Ys) function definedbelow:

If Ys>Ylt; Yc=Ys+Cs (Ys−Ylt)²

else; Yc=Ys.

Since such correcting function Yc=f(Ys) maintains continuity of thederivative of the resulting corrected curve, such transformationmaintains a smooth transition between the non-corrected and correctedregions while it reverses non-linearity occurring originally in thecorrected region due to the gain limitation.

The detailed implementation of such PAAR correction is shown in FIG. 2,wherein the A/D samples are written into the Stage1 of the SynchronousCircular Processor (SCP) comprising A/D_Buffer0/A/D_Buffer1 driven bythe circular sub-clocks 1/Clk0/1/Clk1 accordingly (see FIG. 12Bexplaining circular sub-clocks applications).

Using 2 buffers having separate processing circuits attached enables twotimes longer processing times for calculating DMT0/DMT1 values withreversed effects of the gain limitation.

The Linearity Threshold (LinThr(D:0)) is subtracted from the amplitudeof the attenuated signal sample (i.e. from theModulus(A/D_Buffer(Sign,D:0)) and such subtraction result is squared andadded to the amplitude of the attenuated sample, in order to reversesaid gain attenuation.

Any non-linearity can be reversed smoothly (i.e. without derivativesdiscontinuity) with any accuracy desired by applying polynomialtransformation:Y _(reversed) =C _(s0) Y _(s); if . . . Y _(s)ε (0,Y _(t1)]Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t1))^(e1); if . . . Y_(s)ε (Y _(t1) /Y _(t2)]Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t1))^(e1) +C _(s2)(Y_(s) −Y _(t2))^(e2); if . . . Y _(s)ε (Y _(t2) , Y _(t3)]Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t1))^(e1) +C _(s2)(Y_(s) −Y _(t2))^(e2) + . . .+C _(sN)(Y _(S) −Y _(tN))^(eN); if . . . Y_(S)ε (Y _(t(N-1)) , Y _(tN)]wherein; C_(s0), C_(s1), . . . C_(sN) represent slopes of approximationsadded at 0, Y_(t1), Y_(t2), . . . Y_(tN) non-linearity thresholds.

The implementation and equations shown above illustrate a method forreversal of gain non-linearity and/or signal attenuation, wherein suchmethod comprises:

identification of dependency between processed signal attenuation andattenuated signal amplitude;

defining approximation thresholds and their approximation slopes andapproximation exponents;

calculating an exponential component for every said approximationthreshold exceeded by an attenuated signal sample, by rising adifference, between the attenuated sample and its approximationthreshold, to a power defined by its approximation exponent;

calculating an approximation component for every such approximationthreshold exceeded by an attenuated signal sample, by multiplying suchexponential component by its slope coefficient;

addition of such approximation component, calculated for the particularapproximation threshold, to the approximation result comprising previousapproximation components calculated for previous approximationthresholds exceeded by the attenuated signal sample;

wherein by such addition of the approximation components calculated forthe approximation thresholds exceeded by the distorted and/or attenuatedsignal sample, said gain-non-linearity and/or signal attenuation isreversed.

This disclosure includes an implementation of a Finite Impulse Response(FIR) filter with a circularly driven register (i.e. consecutiveprocessed samples are clocked in circularly into the register) connectedto circuits processing properly delayed samples supplied by theregister. Such register based FIR filter is shown in FIG. 3 wherein theFIR filter is exemplified as the 1-z⁻⁵¹¹ Comb Filter.

The comb filtering based on “1-z⁻⁵¹¹” begins when N+1=512 samplesinitializing a new tone are collected in CFR2(S0:S511), wherein:

the first filtered sample S(511) is filtered with the collected alreadysamples S(0)/S(509) delayed 511/2 times accordingly, in order to producethe output CFSO(Sign,E:0) fulfilling the difference equationv(n)=x(n)−r ^(N) x(n−N)−r ² x(n−2);and similarly the second filtered sample S(0) is filtered with thecollected already samples S(1)/S(510) delayed 511/2 times accordingly,in order to produce the output CFSE(Sign,E:0) fulfilling the samedifference equation.

Said corrected DMT0/DMT1 outputs of the 1^(st) SCP stage are connectedto the Comb Filter Register 2 driven by 512 circular clocks 2/Clk0,2/Clk3, . . . 2Clk511 in order to enable the 1-z⁻⁵¹¹ Comb Filter of512th order implemented by the 2^(nd) SCP stage.

Such comb filter has 511 zeros assigning 511 Sub-Bands which can beproduced by Frequency Sampling Filters constructed by connecting theoutput of such Comb Filter to 511 resonating filters defined by theequations:1/(1−e ^(j2Πk/511) z ⁻¹) for k=0, 1, 2, . . . 510.

Such idea is implemented in more practical way in FIG. 3 where all thedetails are shown and described (such Frequency Sampling Filtering namedas Type IV FSF is explained comprehensively on the pages 311-319 in thebook “Understanding Digital Signal Processing” by Lyons, Ed. 2004”).

Consequently “even zeros” from the range of ˜64 to ˜510 correspond toeven Sub-Bands 64-510 which are considered as facilitating DMT tonesnumbered from 32T to 255T, while “odd zeros” correspond to separatingthem odd Sub-Bands numbered from 63-511 which are considered asfacilitating noise sensing Sub-Tones numbered as 31.5ST, 32.5ST, and33.5ST to 255.5ST.

Such naming convention of the Tones and Sub-Tones is used further on inthis Section text and drawings.

The Comb Filter shown in FIG. 3 uses selection circuits, connected tothe circularly driven Comb Filter Register 2 (CFR2), for producingconsecutive filtered signal samples.

Another possible implementation can use a shifted CFR2 wherein theDMT0/DMT1 signals are clocked into the same segment S0 of the CFR2 andalways the same segments S0/S511 can be used, as providing 511 timesdelay, for producing Comb Filter Output signal.

This disclosure comprises both; the FIR filter, with the circularlydriven filter register, using the selection circuits connected to theregister for supplying consecutive signal samples, and the FIR filter,with the shifted filter register, utilizing the shifting of the filterregister for supplying consecutive signal samples.

This disclosure includes an implementation of an Infinite ImpulseResponse (IIR) filter with a circularly driven filter register (i.econsecutive filtered samples are clocked circularly into the register)supplying IIR processing circuits with properly delayed samples. SuchIIR filter achieves infinite response characteristic by connectingoutputs of such IIR processing circuits back to the inputs of thecircularly driven register.

Said IIR Filter with circularly driven register (see FIG. 4), usesselection circuits, connected to the outputs of the Resonator FilterRegister (129RFR(S0:S3)), for supplying filter processing circuits whichproduce consecutive filtered signal samples written back circularly intoconsecutive samples S0-S3 of 129RFR (S0:S3).

Such circularly driven IIR filter exemplified in FIG. 4, is a resonatingfilter, having idealistic transfer function(F(z)=1/(1−e^(j2Π258/511)z⁻¹)) adjusted into the Type IV FSF (explainedin the Lyons book mentioned above) for better stability and performance.

Another possible implementation can use a shifted Resonator FilterRegister (RFR(S0:S3)) wherein the input signal from the previous stageand outputs of the Resonator Filter Register supply filter processingcircuits which produce filtered sample clocked into the same segment S0of the RFR(S0:S3).

This disclosure comprises both; the IIR filter, with the circularlydriven register, using the selection circuits connected to the registeroutputs for supplying consecutive processed samples, and the IIR filter,with the shifted register, using shifted register outputs for supplyingconsecutive processed samples to the filter processing circuits.

The odd/even output of the comb filter CFS0(Sign,E:0)/CFSE(Sign,E:0)re-timed in the Comb Filter Reg.3 (CFR3) produces Resonant FiltersSelected Input (RFSI(S,E:0)) which is connected to the multipleresonating Infinite Impulse Response (IIR) Filters designated forspecific Tones or Sub-Tones.

Such resonating IIR filter designated for the 129 Tone (129T) is shownin FIG. 4, wherein:

the reference “(from 129T_CRS)” indicates that any following constant isprovided by its register (belonging to the Control Register Set for 129Tone), wherein this register is loaded by PCU in order to controloperations of the Real Time Processor for 129 Tone (129T_RTP);

the coefficient k equals to 2×129=258 for the 129 Tone;

the resonating IIR filtering begins after the CFR3(S0) is produced aftercollecting N+1=512 samples in CFR2(S0:S511);

the Resonator Filter Register is reset by the signal RESET_RFR(S0:S3)before any new tone IIR filtering begins,

and furthermore such IIR filtering of an entire sequence of N+1=512samples is completed before using resulting RFR outputs for any furthersignal processing.

Similar resonating IIR filter designated for the 129.5Sub-Tone (129.5ST)is shown in FIG. 4A, wherein:—

the reference “(from 129.5 ST_CRS)” indicates that any followingconstant is provided by its register (belonging to the Control RegisterSet for 129.5ST), wherein this register is loaded by PCU in order tocontrol operations of Real Time Processor for 129.5 Sub-Tone(129.5ST_RTP);

the coefficient k equals to 2×129.5=259 for the 129.5 Sub-Tone;

the resonating IIR filtering begins after the CFR3(S0) is produced aftercollecting N+1=512 samples in CFR2(S0:S511);

the Resonator Filter Register is reset by the signal RESET_RFR(S0:S3)before any new tone IIR filtering begins,

and furthermore such IIR filtering of an entire sequence of N+1=512samples is completed before using resulting RFR outputs for any furthersignal processing.

This disclosure comprises implementation of integrating and/or averagingtime domain filter with a circularly driven register (i.e consecutiveprocessed samples are clocked in circularly into the register) supplyingsuch filter's integrating/summating circuits with a proper set ofintegrated/summated samples.

Such time domain filter achieves integration/summation over aconsecutive set containing a required number of samples, by circularreplacing of the first sample of a previous set, stored in the circularregister, with a new sample following the last sample of the previousset. Resulting consecutive set of samples on the circular registeroutputs is supplied to the filter integrating/summating circuitproducing filter output.

Such time domain filter is exemplified in FIG. 5 where it is used forintegration of 129T Half-Cycles and for detecting phases of suchHalf-Cycles (HC) ends, wherein an end of the present HC occurs at thebeginning of the next opposite HC.

Since the input to such HC integrating filter has already been filteredby the previous stages FSF, such input must have sinusoidal shape.Therefore resulting integral of amplitudes of 129T HC representsfiltered indicator of original amplitude of the 129T sinusoid. Suchintegral is used for the recovery of the original tone amplitude as itexplained later on.

Since such time domain filter and all the previous filters belong to theSCP operating in phase with the Tones Frame (DMT Frame), such detectedHC phase is used for recovering phase of the originally transmitted HCof 129T.

The outputs of the 129T Resonator Filter Register (129RFR(S0:S3)) areclocked in circularly into the Stage5/129 Half Cycle Register(129HCR(S0:S15)) which comprises 16 samples covering an approximated HCinterval.

The outputs of 129HCR are connected to the summating circuits producingan integral of the last 16 samples long sequence (named Next Integer(NI)).

While Next Integral (NI) of amplitudes of HC long interval is calculatedand fed to the Integral Register (IR(0:K)); it is also compared with thePrevious Integral (PI) kept in Previous Integral Buffer (PIB(0:K)), inorder to verify if Half-Cycle end is reached.

Such HC end occurs when NI<PI/NI>PI is detected followingpositive/negative HC accordingly. When the end of positive/negative HCis detected, the integral of amplitudes over positive/negative HC isloaded into 129 Positive Ampl. Reg. ((129PAR((0:K))/129 Negative Ampl.Reg. (129NAR(0:K)) by signal 129Ld_PA/FE/129Ld_NA/RE accordingly.

Signals 129Ld_PA/FE/129Ld_NA/RE are generated by DecCLK/IncCLK strobes,only if IncCTR>5/DecCTR>5 condition is met. The purpose of suchpreconditioning is prevention of oscillations (such as caused bycomputational instability at small signal amplitudes), by providinghisteresis introduced by Inc. Counter(0:2)/Dec. Counter(0:2) forpositive/negative HC accordingly.

Said IncCTR>5/DecCTR>5 conditions are possible only when the multi-toneprocessing inhibition signal MTP_Inh is de-activated after initial 640sampling periods of every new DMT frame (see FIG. 5 and FIG. 6).

The 642 Decoder (shown in the block 8 in FIG. 6) decodes such 640samples delay introduced by waiting until 640+2 sampling intervals arecounted by Frame Samples Counter (FSC), wherein the additional 2intervals account for the 2 sampling intervals occurring between theMTP_Inh generation in the SCP stage 4 and its actual application in theSCP stage 6.

In addition to the prevention of IncCTR>5/DecCTR>5 conditions, MTP_Inhsignal inhibits any generation of 129Ld_PHC/129Ld_NHC (see FIG. 6), andthus MTP_Inh makes sure that no time domain processing takes placebefore valid signals are supplied by the frequency domain filters.

Shown in FIG. 5A circuit providing “Half_Cycles Integration & AmplitudesRegistration for 129.5 Tone”, performs the same operations as thecircuit shown in FIG. 5 described above.

Since SCP operations are driven by clocks and sub-clocks having knownphase and frequency relation to DMT Frame, results produced by SCPstages have known phase relations to DMT Frame as well. Therefore suchdetection of an end of positive/negative HC can be used to detect phaseof Tone cycle producing such HC.

As such detection of positive/negative HC end signals detection offalling/rising edge of 129 Tone sinusoid as well, signal129Ld_PA/FE/129Ld_NA/RE is used in FIG. 6 for capturing phase of suchfalling/rising edge by capturing 129 Tone Phase in 129 Falling Edge Reg.(129FER(0:5))/129 Rising Edge Reg. (129RER(0:5) accordingly.

This FIG. 6 shows Phase Capturing and Tone Processing Initialization forthe 129T/128.5ST/129.5ST, wherein:

the reference “(from 129T_CRS)” indicates that any following constant isprovided by its register (belonging to the Control Register Set for 129Tone), wherein this register is loaded by PCU in order to controloperations of Real Time Processor for 129 Tone (129T_RTP);

since 129RisingEdgeReg./129FallingEdgeReg. captures the end of anegative/positive half-cycle, it represents phase of the rising/fallingedge accordingly of a sinusoid represented by such negative/positivehalf-cycle.

Such 129 Tone Phase is produced by subtracting 129 Last Cycle Phase Reg.(such 129LCPR(0:13) specifies nr. of sampling intervals corresponding tothe beginning of the presently expected cycle of 129 Tone) from FrameSamples Counter (such FSC(0:12) specifies nr. of sampling intervalswhich past from the beginning of the present DMT Frame).

Consequently such capture of the 129 Tone Phase defines phase ofpresently detected cycle of 129 Tone measured in number of samplingintervals which occurred between the beginning of the expected cycle(having 0 phase) and the detected 129T cycle.

Content of 129LCPR is derived by comparing if FSC-LCPR=129 Cycle (129Cycle represents number of sampling intervals expected duringconsecutive 129 Tone cycle), and by loading FSC into LCPR whenever suchequality condition is fulfilled.

In order to avoid accumulation of digitization errors during suchmultiple comparisons (involving fractional numbers expressing expectedlengths of 129 Tone cycles); a method using fractional bit staffing(described also in public domain) can be applied by adding consecutivebits from Fractional Bits Register (FBR(0:128)) to 129 CycleBase(0:4).These additions provide consecutive values of 129 Cycle(0:5) keepingtotal digitization error below single sampling interval.

SCP combines in-phase processing in frequency domain with in-phaseprocessing in time domain.

Therefore SCP detects time/phase dependence between noise sub-bands andDMT Tones.

Consequently SCP enables estimating and compensating impact of neighbornoise sub-bands and neighbor tones on specific cycles of particulartones.

Such estimates and compensation use data from training session and fromadaptive wave-form sampling and screening for identifying noise patternsand for programming compensation and inverse transformation coefficientsby PCU.

Such detection of phase relations is facilitated by capturing a fallingedge of positive HC of 129T/128.5ST/129.5ST in129FER(0:5)/128.5FER(0:5)/129.5FER(0:5) by signal129Ld_PA/FE/128.5Ld_PA/FE/129.5Ld_PA/FE.

Similarly a rising edge of negative HC of 129T/128.5ST/129.5ST iscaptured in 129RER(0:5)/128.5RER(0:5)/129.5RER(0:5) by signal129Ld_NA/RE/128.5Ld_NA/RE/129.5Ld_NA/RE.

In order avoid using incomplete HC detected at a beginning of DMT Frame,second appearance of signal LD_PA/FE/LD_NA/RE is required in order toproduce signal 129Ld_PHC/129Ld_NHC enabling further processing of 129Tone shown in FIG. 7.

This FIG. 7 shows Retiming & Averaging of Positive and Negative HC for129T/128.5ST/129.5ST, wherein:

the content of 129FER/128.5FER/129.5FER is processed by the“Modulo-Cycle Adder of Half-Cycle” converting a phase of falling edgeinto a corresponding phase of rising edge,

wherein this phase of rising edge represents phase of sinusoid definedby a positive half-cycle ending at the time instant captured in129FER/128.5FER/129.5FER.

The 7/Clk shown in FIG. 7 generates single PHC/Clk/CYC/Clk impulse if itdetects Ld_PHC/Ld_NHC timed by 6/Clk. Such PHC/Clk re-times129RER(0:5)/128.5RER(0:5)/129.5RER(0:5) by re-loading them into129REB(0:5)/128.5REB(0:5)/129.5REB(0:5), which are:

averaged with 129FER(0:5)/128.5FER(0:5)/129.5FER(0:5) converted intocycle edge by Modulo-Cycle Addition of Half-Cycle);

and re-timed with CYC/Clk loading them into129AER(0:5)/128.5AER(0:5)/129.5AER(0:5).

The positive amplitude registers 129PAR(0:K)/128.5PAR(0:K)/129.5PAR(0:K)are averaged with 129NAR(0:K)/128.5NAR(0:K)/129.5NAR(0:K) accordinglyand loaded into the averaged amplitude registers129AAR(0:K+1)/128.5AAR(0:K+1)/129.5PAR(0:K+1).

SCP comprises using every positive or negative HC as separate data usedfor recovering a tone symbol. Such ability of using singular Half-Cyclesfor data recovery provides a huge data redundancy which facilitates useof statistical methods much more reliable than conventionally used DFTaveraging over DMT Frame.

Nevertheless, in order to illustrate implementation having lower powerdissipation; SCP exemplified by this embodiment has 7th stage (see FIG.7) combining amplitudes and phases of positive and negative HC intoaverages per cycle (which still provide significant redundancy).

The NFIT comprises an inversion of frequency related distortions in atransmission channel (such as DMT link), by applying differentnormalizing coefficients to different Carrier Frequencies (such as DMTTones) wherein such normalizing coefficients are adjusted to equalizeamplitude and phase distortions of the transmitted Carrier Freq.including distortions introduced by a signal processing applied; suchinverse normalization of amplitudes and phases comprises:

identification of the frequency related distortions occurring on theCarrier Frequencies (or DMT Tones) by using training sessions oradaptive wave-form sampling/screening controlled by PCU;

calculating normalizing coefficients, for such Carrier Frequencies orDMT Tones, by PCU;

using such normalizing coefficients, supplied by PCU, by real-timeprocessing unit for equalizing such frequency related distortions in theprocessed Carrier Freq. or DMT Tones.

Such amplitude and phase normalization for 129T/128.5ST/129.5ST is shownin FIG. 8, wherein it includes normalization of noise sensing Sub-Tones(128.5ST/129.5ST) neighbor to the data carrying 129T.

129 Tone phase defined by 129 Tone Averaged Edge Register (129AER(0:5)),is normalized by multiplying by the 129T Phase Normalizing coefficient(129PhaNor) and by adding the 129T Phase Adjusting coefficient(129PhaAdj).

Since sinusoidal noise contribution from such neighbor sub-tones isdependent on phase differences between the tone and the sub-tones, suchphase differences are normalized by multiplying them by the PhaseNormalizing coefficient.

129T Averaged Amplitude Register (129AAR(0:K+1)) and its 128.5ST/129.5STcounterparts ((128.5AAR(0:K+1)/(129.5AAR(0:K+1)) are normalized bymultiplying them by the 129T Amplitude Normalizing coefficient (129AmpNor).

All such normalizing coefficients are taken from the 129 Tone ControlRegister Set (129T_CRS) which is pre-loaded by PCU implementing adaptivedistortion reversing techniques.

While SCP comprises performing signal processing operations which aresynchronized by the processed incoming signal, such approach comprisestwo different synchronization methods specified below and exemplified bythe embodiments shown herein.

When SCP stages (such as previous 7 stages) perform processing ofbelonging to frequency domain DMT Tones (or Multi-Band carriers); theyare synchronized by DMT Frame (or channel frame), as such stages aredriven by the clocks or sub-clocks synchronous to the sampling clockwhich is phase locked to DMT Frame (or channel frame).

When SCP stages (such as this 8^(th) stage and next stages) performprocessing of already detected tone (or band) cycles belonging to timedomain; they are synchronized by such cycles detection events instead,as such stages are driven by clocks generated when information aboutcycle detection is passed from a higher level stage to the next level.

Such second synchronization method does not do (discontinues) anyfurther processing if a new cycle of the tone (or band) is not detected.

SCP comprises both synchronization methods defined above.

The cycle detection signal CYC/Clk enables using leading edge of 8/Clk/8(having frequency 8 times lower than the sampling clock) for the onetime activation of AS1/Clk signal which drives all the registers of theSCP 8^(th) stage presented in FIG. 8.

Such AS1/Clk signal remains active (for about 1 sampling period) untilthe leading edge of the next 9/Clk signal activates the AS1_RST signal(see FIG. 9A).

Such AS1_RST signal enables using leading edge of the next 8/Clk/8 forthe one time activation (for about 1 sampling period) of the signalwhich initiates reading of amplitude and noise compensation coefficientsfrom Memory of Noise Compensation Coefficients (MNCC).

Such timing enables Address Decoders for Memory of Noise CompensationCoeff. (AD_MNCC) to have processing time extended to 8 samplingintervals in order to use normalized amplitudes and phases provided bythe previous 8th stage for decoding Address(0:8)/NS_MNCC beforeAS2/Read_MNCC is activated.

The NFIT comprises an efficient non-linear reversing of transmissionchannel distortions and non-linear noise compensation in over-sampledsignals, by implementing real time processing units (RTPs) usingsimplified algorithms applying variable coefficients, wherein such RTPsare controlled by the back-ground processing PCU which implementsadaptive non-linear algorithms by analyzing received line signal andintermediate RTPs processing results and by defining and down-loadingsuch coefficients to content addressed memories accessed by RTPs such a129 Tone Control Registers Set (129T_CRS) or Memory of NoiseCompensation Coefficients (MMCC).

Such NFIT noise compensation method comprises RTP operations listedbelow:

frequency domain and/or time domain processing of data carrying signaland/or neighbor tones or frequency bands in order to derive estimates ofparameters influencing distortion or noise components in the signal,wherein such parameters may include amplitudes and/or phase of datacarrying tone or freq. band and/or surrounding noise or interferencefrom neighbor tones or bands;

converting such parameters into an effective address of said contentaddressed memory in order to access coefficients providing most accuratecompensation of said channel distortion or noise;

applying such coefficients to a sequence of predefined arithmetic and/orlogical operations performed on the received signal in order to reversetransmission channel distortions and/or to improve signal to noiseratio.

Such noise compensation method is illustrated in FIG. 9A-FIG. 9B andFIG. 10 showing stage 9th and 10th of the SCP embodiment.

It is shown in FIG. 9A that:

said noise affecting parameters supplied by 129T/129.5ST NormalizedAmplitude Registers (abbreviated to 129NAR(0:P)/129.5NAR(0:P)) and129.5ST Averaged Phase Difference Register (abbreviated to129.5APDR(0:L), are used together with the 129 Tone AmplitudeThresholds/Next Sub-tone Amplitude Thresholds and Next-Sub-tone PhaseDifference Thresholds, in order to decode address to the Next Sub-toneMNCC (Address(0:8)/NS_MNCC).

It is detailed in FIG. 9A that:

said 129 Tone Amplitude Thresholds facilitating use of differentcoefficients programmed adaptively by PCU, are applied as T/AT1, T/AT2,. . . T/AT6, T/AT7;

said Next Sub-tone Amplitude Thresholds facilitating use of differentcoefficients depending on 129.5ST Amplitude, are applied as NS/AT1,NS/AT2, . . . NS/AT6, NS/AT7;

said Next Sub-tone Phase Difference Thresholds facilitating use ofdifferent coefficients depending on 129.5ST Phase Difference, areapplied as NS/PDT1, NS/PDT2, . . . NS/PDT6, NS/PDT7;

said Address(0:8)/NS_MNCC selects reading & loading of coefficients,compensating expected noise contribution from the 129.5ST, to theirregisters 129.5Ampl. Addition Reg/129.5 Ampl. Multiplication Reg./129.5Phase Addition Reg./129.5 Phase Multiplication Register.

Very similar circuits and methods (shown in FIG. 9B) addressing thePrevious Sub-tone MNCC (see Address(0:8)/PS_MNCC) are applied in orderto select & load coefficients, compensating expected noise contributionfrom the 128.5ST, to their registers 128.5Ampl. Addition Reg/128.5 Ampl.Multiplication Reg./128.5 Phase Addition Reg./128.5 Phase MultiplicationRegister.

These registers, loaded from NS_MNCC and PS_MNCC, supply coefficientsproducing estimates of noise compensating components which are added to129T amplitude and to 129T phase (as shown in FIG. 10), in order toprovide compensated amplitude in 129 Compensated Amplitude Register(129CAR(0:P,ERR)) and compensated phase in 129 Compensated PhaseRegister (129CPR(0:L,ERR)).

Such noise compensating coefficients are derived by PCU based onevaluations of noise patterns occurring in tones frequency region andtheir contributions to signal noise acquired during training session andsupported by adaptive wave-form sampling and screening utilizing widecoverage of almost entire spectrum by Tones and Sub-tones detected withsaid FSFs.

The NFIT comprises:

detecting noise patterns occurring in frequency domain by usingfrequency domain processing such as Frequency Sampling Filters for noisesensing in a wide continues frequency spectrum incorporating datacarrying tones or frequency bands;

detecting noise patterns occurring in time domain by using time domainprocessing for noise sensing over time intervals including tone (orband) reception intervals;

using back-ground PCU for analyzing such detected noise patterns and forcreating deterministic and random models of occurring noise patterns;

using such models of noise patterns for deriving noise compensationcoefficients used by the Real Time Processors for improving signal tonoise ratios in received data carrying signal;

taking advantage of the recovered symbols redundancy (assured by theRTPs time domain processing ability of recovering data symbol from everytone cycle) by applying such noise models for estimating probability ofsymbols recovered and/or for dismissing symbols accompanied by highnoise levels close in time;

using such probability estimates and/or dismissals of unreliable symbolsfor applying statistical methods which are more reliable thanconventional DFT averaging of tone signal received.

Such ability of said symbol dismissal, if detected in a vicinity of highnoise, is illustrated in FIG. 10, wherein:

the comparison is made if the sum of 129T Amplitude Noise Components(128.5/129.5 Amp.NoiseComp.) exceeds 129 Maximum Ampl. Noise(abbreviated to 129Max.AmpNoise) pre-loaded to 129T-CTRS by PCU as atotal limit on both acceptable compensations from 128.5ST and 129.5STtaken together.

if the comparison 128.5ANC+129.5ANC>129MAN, the ERROR bit marking suchsymbol for dismissal is written to the 129CAR(0:P,E).

Similarly for the 129T Phase Noise Components(128.5/129.5Pha.NoiseComp.):

if the comparison 128.5PNC+129.5PNC>129 MPN, the ERROR bit marking suchsymbol for dismissal is written to the 129CAP(0:L,E).

The NFIT comprises a method for recovery of data symbol transmitted by asingular half-cycle/cycle of said DMT or Multiband tone, wherein:

an amplitude measure of said singular half-cycle/cycle, such as integralof amplitude over the half-cycle/cycle time period, and a phase measureof the half-cycle/cycle, are applied to a symbol decoder transformingsuch combination of amplitude and phase measures into a numberrepresenting said recovered data symbol.

Such symbol recovery method further comprises:

comparing said amplitude measure to predefined amplitude thresholds, inorder to decode an amplitude related factor in a recovered symboldefinition;

comparing said phase measure to predefined phase thresholds, in order todecode a phase related factor in recovered symbol definition;

wherein such amplitude and phase comparators produce their parts of abinary address to a content addressed memory storing a counter ofhalf-cycles/cycles detecting said symbol occurrences during said DMT orMulti-band signal frame;

wherein such symbols counters memory (SCM) can accommodate differentsymbols, detected during said DMT or Multi-band frame, varying duringthe same frame due to said channel distortions and changing in timenoise distribution;

sorting symbols, carried by singular half-cycles/cycles, detected duringsaid DMT or Multi-band frame, in accordance to their detections numbersand/or distributions;

application of statistical methods for selecting data symbol recovered,from said DMT or Multiband tone, such as selection of a symbol havinghigher detections number in a range outlined with statisticaldistribution models.

Implementation of such data symbol recovery, is exemplified in FIG. 11.

A DMT control registers set (DMT_CRS) programmed adaptively by PCU,supplies said amplitude thresholds (AT1, AT2, AT3, AT4) and said phasethresholds (PT1, PT2, PT3, PT4) to address decoder for symbols countsmemory (AD_SCM); wherein:

said AT1, AT2, AT3, AT4 (programmed adaptively by PCU) representAmplitude Thresholds digitizing recovered amplitude;

said PT1, PT2, PT3, PT4 (programmed adaptively by PCU) represent PhaseThresholds digitizing recovered phase.

AD_SCM digitizes compensated amplitude/phase provided byCAR(0:P)/CPR(0:L) by comparing them with said amplitude and phasethresholds, in order to produce address ADR(0:3) equal to binary code ofsymbol detected.

Such ADR(0:3) is applied (as ADR/SCM) to the symbols counts memory (SCM)when the read-write signal (Rd-Wr/SCM) initializes read-write cycle in129T symbol counts memory (129SCM).

In response to such Rd-Wr/SCM signal said 129SCM provides a content of asymbol counter (129Symb.Count(0:8)) identified by said ADR/SCM.

129Symb.Count is increased by 1 and is written back to the same symbollocation in SCM (as updated counter CNT-UPD(0:8)/SCM), if 129SymAcc=1(i.e. if both Error Bits CAR(E) and CPR(E) are inactive).

However; 129Symb.Count remains unchanged when it is written back to thesame SCM location, if 129SymAcc=0 (i.e. if CAR(E) or CPR(E) is active).

Maximum Count of detections of the same symbol discovered in present129T, is stored in 129Max.CounterReg. (129MCR(0:8)) which is read by PCUat the end of DMT frame.

Any consecutive updated counter CNT-UPD/SCM (abbreviated as 129SC+1) iscompared with such 129Max.CounterReg. (abbreviated as 129MCR).

If (129SC>129MCR)=1; the newly updated counter is loaded to said129Max.CounterReg., and the address of the newly updated counter (equalto the binary code of the symbol detected) is loaded to129Max.Cont.Addr.Reg. (129MCAR(0:3) which is read by PCU at the end ofDMT frame.

Otherwise if (129SC>129MCR)=0; both 129Max.CounterReg. and129Max.Cont.Addr.Reg. remain unchanged.

In order to simplify further PCU operations; there is a 129T detectedsymbols map register (129DSMR(1:16)) which has 16 consecutive bitsdesignated for marking occurrence of the 16 consecutive symbols duringDMT frame, wherein particular marking bit is set to 1 if correspondingsymbol occurs one or more times. Such 129DSMR(1:16) is read by PCU atthe end of DMT frame.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein, but is to beaccorded the full scope consistent with the claims, wherein reference toan element in the singular is not intended to mean “one and only one”unless specifically so stated, but rather “one or more”. All structuraland functional equivalents to the elements of the various embodimentsdescribed throughout this disclosure that are known or later come to beknown to those of ordinary skill in the art are expressly incorporatedherein by reference and are intended to be encompassed by the claims.Moreover, nothing disclosed herein is intended to be dedicated to thepublic regardless of whether such disclosure is explicitly recited inthe claims.

1. A method for data recovery from a composite signal carrying acomposite frame (DRCS) comprising discrete multiple tones (DMT) ormultiple sub-bands (MSB) wherein said data recovery comprises signalprocessing in frequency domain and in time domain, the DRCS methodcomprising the steps of: processing the composite signal in frequencydomain in order to recover said tones or sub-bands, wherein a knownphase relation is maintained between the recovered tones or sub-bandsand the composite frame; processing the recovered tones or sub-bands intime domain in order to measure amplitudes and phases of singularhalf-cycles or cycles of the recovered tones or sub-bands; transformingthe measured amplitudes and phases by reversing distortions introducedby a transmission channel of the composite signal; using the transformedamplitudes and phases for recovering data symbols encoded originally ina transmitted composite signal.
 2. A DRCS method as claimed in claim 1,wherein the DRCS method further comprises the step of: selecting a datasymbol encoded in a particular tone or sub-band by using statisticalmethods for processing a plurality of symbols recovered from themeasured amplitudes and phases of half-cycles or cycles of theparticular tone or sub-band.
 3. A method for data recovery from acomposite signal (DRCS) carrying a composite frame comprising discretemultiple tones (DMT) or multiple sub-bands (MSB), comprising signalprocessing in frequency domain and time domain wherein amplitudes orphases of a noise or other signals, surrounding a particular datacarrying tone or sub-band in a transmission channel, are detected anddistortions of said particular tone or sub-band are reversed; the DRCSmethod comprising the steps of: processing the composite signal infrequency domain, in order to recover said tones or sub-bands, wherein aknown phase relation is maintained between the recovered tones orsub-bands and the composite frame; processing the tones or sub-bands intime domain, in order to measure amplitudes and phases of singularhalf-cycles or cycles of the tones or sub-bands; detecting saidamplitudes or phases of noise or other signals surrounding saidparticular tone or sub-band; using the detected amplitudes or phases forderiving estimates of distortions introduced by the surrounding noise orother signals to the particular tone or sub-band; using the distortionsestimates for performing a reverse transformation of said measuredamplitudes and phases into amplitudes and phases corresponding toamplitudes and phases encoded originally in a transmitted compositesignal; using the reversely transformed amplitudes and phases forrecovering data symbols encoded originally in the transmitted compositesignal.
 4. A method for data recovery from a received composite signalcarrying composite frame (DRRC) comprising discrete multiple tones (DMT)or multiple sub-bands (MSB), wherein said data recovery comprises signalprocessing in frequency domain and in time domain, the DRRC methodcomprising the steps of: using a sampling clock, synchronous in phase tothe composite frame, for oversampling said received composite signal;using clocks, synchronous to the composite frame, for processing theoversampled signal in frequency domain, in order to recover said tonesor sub-bands having a known phase relation to the composite frame;processing the recovered tones or sub-bands in time domain, in order tomeasure amplitudes or phases of singular half-cycles or cycles of therecovered tones or sub-bands; using a plurality of the measuredamplitudes or phases of singular half-cycles or cycles of a particulartone or sub-band, for a recovery of an original data symbol transmittedby the particular tone or sub-band.
 5. A method for synchronous datarecovery (SDR) from a composite signal carrying a composite framecomprising discrete multiple tones (DMT) or multiple sub-bands (MSB),wherein said data recovery comprises signal processing in frequencydomain and in time domain, the SDR method comprising the steps of:processing said composite signal in frequency domain in order to recoversaid tones or sub-bands, wherein the frequency domain processing isdriven by clocks or sub-clocks synchronous in phase to the compositeframe in order to maintain a known phase relation between the recoveredtones or sub-bands and the composite frame; processing the recoveredtones or sub-bands in time domain, in order to measure amplitudes orphases of singular half-cycles or cycles of the recovered tones orsub-bands; using a plurality of said measured amplitudes or phases ofsingular half-cycles or cycles of a particular tone or sub-band, for arecovery of an original data symbol transmitted by the particular toneor sub-band.